on_off = np.zeros(174)
real_times,on_off[:-1] = np.linspace(0,432.5,173+1),neural_prediction
hrf_function,TR,record_cuts= hrf_single, 2.5 ,np.linspace(0,432.5,173+1)
#
testconv_4_1 = np_convolve_30_cuts(real_times,on_off,hrf_function,TR,record_cuts,cuts=1)
testconv_4_15 = np_convolve_30_cuts(real_times,on_off,hrf_function,TR,record_cuts,cuts=15)
testconv_4_30 = np_convolve_30_cuts(real_times,on_off,hrf_function,TR,record_cuts,cuts=30)
#-------#
# fifth #
testconv_5 = fast_convolution(all_tr_times,neural_prediction,fast_hrf,all_tr_times)
#######################
# c. Plot Comparision #
#######################
plt.plot(all_tr_times,testconv_np,label="conv_np")
plt.plot(all_tr_times,testconv_2,label="user 2")
plt.plot(all_tr_times,testconv_3,label="user 3")
plt.plot(np.linspace(0,432.5,174),testconv_4_1,label="user 4, cut = 1")
plt.plot(np.linspace(0,432.5,174),testconv_4_15,label="user 4, cut = 15")
plt.plot(np.linspace(0,432.5,174),testconv_4_30,label="user 4, cut = 30 (standard)")
plt.plot(all_tr_times,testconv_5,label="user 5")
plt.title("User-made functions matching np.convolve in np.convolve territory")
hrf_function,
TR,
record_cuts,
cuts=15)
testconv_4_30 = np_convolve_30_cuts(real_times,
on_off,
hrf_function,
TR,
record_cuts,
cuts=30)
#-------#
# fifth #
testconv_5 = fast_convolution(all_tr_times, neural_prediction, fast_hrf,
all_tr_times)
#######################
# c. Plot Comparision #
#######################
plt.plot(all_tr_times, testconv_np, label="conv_np")
plt.plot(all_tr_times, testconv_2, label="user 2")
plt.plot(all_tr_times, testconv_3, label="user 3")
plt.plot(np.linspace(0, 432.5, 174), testconv_4_1, label="user 4, cut = 1")
plt.plot(np.linspace(0, 432.5, 174), testconv_4_15, label="user 4, cut = 15")
plt.plot(np.linspace(0, 432.5, 174),
testconv_4_30,
label="user 4, cut = 30 (standard)")
plt.plot(all_tr_times, testconv_5, label="user 5")
plt.title("User-made functions matching np.convolve in np.convolve territory")
cuts=15)
scaled_4_15 = (conv_4_15 - np.mean(conv_4_15)) / (2 * np.std(conv_4_15)) + .4
conv_4_30 = np_convolve_30_cuts(real_times,
on_off,
hrf_function,
TR,
record_cuts,
cuts=30)
scaled_4_30 = (conv_4_30 - np.mean(conv_4_30)) / (2 * np.std(conv_4_30)) + .4
#-------#
# fifth #
#-------#
conv_5 = fast_convolution(cond_all, np.ones(len(cond_all)), fast_hrf,
np.linspace(0, 239 * 2 - 2, 239))
scaled_5 = (conv_5 - np.mean(conv_5)) / (2 * np.std(conv_5)) + .4
############################
# Plot for comparisions #
############################
plt.scatter(cond_all,
np.zeros(len(cond_all)),
color=colors,
label="stimuli instances")
plt.plot(all_tr_times, scaled_np, label="np naive approach scaled")
plt.plot(cond_all, scaled_2, label="user 2 scaled")
plt.plot(all_tr_times, scaled_3, "-o", label="user 3 scaled")
plt.plot(all_tr_times,
scaled_4_30,
def test_convolution():
#################
# i. Can the user-created functions match np.convolve in np.convolve territory
TR = 2.5
tr_times = np.arange(0, 30, TR)
hrf_at_trs = np.array([hrf_single(x) for x in tr_times])
n_vols = 173
neural_prediction = events2neural(location_to_class_data+'ds114_sub009_t2r1_cond.txt',TR,n_vols)
all_tr_times = np.arange(173) * TR
##################
# a. np.convolve #
##################
testconv_np = np.convolve(neural_prediction, hrf_at_trs) # hrf_at_trs sample data
N = len(neural_prediction) # N == n_vols == 173
M = len(hrf_at_trs) # M == 12
testconv_np=testconv_np[:N]
#####################
# b. user functions #
#####################
#--------#
# second #
testconv_2 = convolution(all_tr_times,neural_prediction,hrf_single)
#-------#
# third #
testconv_3 = convolution_specialized(all_tr_times,neural_prediction,
hrf_single,all_tr_times)
#--------#
# fourth #
on_off = np.zeros(174)
real_times,on_off[:-1] = np.linspace(0,432.5,173+1),neural_prediction
hrf_function,TR,record_cuts= hrf_single, 2.5 ,np.linspace(0,432.5,173+1)
#
testconv_4_1 = np_convolve_30_cuts(real_times,on_off,hrf_function,TR,record_cuts,cuts=1)
testconv_4_15 = np_convolve_30_cuts(real_times,on_off,hrf_function,TR,record_cuts,cuts=15)
testconv_4_30 = np_convolve_30_cuts(real_times,on_off,hrf_function,TR,record_cuts,cuts=30)
#-------#
# fifth #
testconv_5 = fast_convolution(all_tr_times,neural_prediction,fast_hrf,all_tr_times)
additional_runs=[testconv_np,testconv_2,testconv_3,testconv_4_1,testconv_4_15,testconv_4_30,testconv_5]
names=["testconv_np","testconv_2","testconv_3","testconv_4_1","testconv_4_15","testconv_4_30","testconv_5"]
print("Max difference between model and testconv_np:")
for i,my_convolved in enumerate(additional_runs):
if my_convolved.shape[0]==testconv_np.shape[0]:
print(names[i],max(abs(testconv_np-my_convolved)))
else:
print(names[i],max(abs(testconv_np-my_convolved[:-1])))
# Actual asserts
for i,my_convolved in enumerate(additional_runs):
if my_convolved.shape[0]==testconv_np.shape[0]:
assert (max(abs(testconv_np-my_convolved) < .0001))
else:
assert (max(abs(testconv_np-my_convolved[:-1]) < .0001))
real_times,on_off = cond_all,np.ones(len(cond_all)) hrf_function,TR,record_cuts= hrf_single, 2 ,np.linspace(0,239*2-2,239) conv_4_15 = np_convolve_30_cuts(real_times,on_off,hrf_function,TR,record_cuts,cuts=15) scaled_4_15=(conv_4_15-np.mean(conv_4_15))/(2*np.std(conv_4_15)) +.4 conv_4_30 = np_convolve_30_cuts(real_times,on_off,hrf_function,TR,record_cuts,cuts=30) scaled_4_30=(conv_4_30-np.mean(conv_4_30))/(2*np.std(conv_4_30)) +.4 #-------# # fifth # #-------# conv_5 = fast_convolution(cond_all,np.ones(len(cond_all)),fast_hrf,np.linspace(0,239*2-2,239)) scaled_5=(conv_5-np.mean(conv_5))/(2*np.std(conv_5)) +.4 ############################ # Plot for comparisions # ############################ plt.scatter(cond_all,np.zeros(len(cond_all)),color=colors,label="stimuli instances") plt.plot(all_tr_times,scaled_np,label="np naive approach scaled") plt.plot(cond_all,scaled_2,label="user 2 scaled") plt.plot(all_tr_times,scaled_3,"-o",label="user 3 scaled") plt.plot(all_tr_times,scaled_4_30,"-*",label="user 4 scaled (30 cuts)",color="k") plt.plot(all_tr_times,scaled_4_15,"-*",label="user 4 scaled (15 cuts)")
